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Reshma Jagsi will be a Keynote Speaker at “Strategies to Empower Women to Achieve Academic Success," which will be held June 7th (8:30 a.m. – 11 a.m., A. Alfred Taubman Biomedical Science Research Building). The event is sponsored by the A. Alfred Taubman Medical Research Institute.

Vaccine refusal has an impact on public health; however, research has shown that it is very difficult to change attitudes towards vaccines. People are often hesitant about vaccines because they don’t trust that potential harms are documented and reported. The question is: how can we increase trust and vaccine utilization?

Brian Zikmund-Fisher was interviewed by Reuters Health for the article "Shared decision making still lacking for cancer screening." He discusses his research and trade-offs in cancer screenings. "What this study does is it shows that despite all of the initiatives and the discussion of shared decision making that has been going on, we don't seem to be moving the needle very much," he states.

His interview also received press in the Chicago Tribune and New York Daily News.

A 5% chance of death or a 10% chance of death: which would you choose?

Imagine that you have been diagnosed with a slow growing cancer. Right now, the cancer is not causing you to feel sick. For most people, the cancer will grow so slowly it will never cause them any trouble. For others, the cancer will grow to the point that it makes them sick. Untreated, five percent (5 out of 100) will die of the cancer. Your doctor tells you that you have two treatment options: watchful waiting or surgery. Watchful waiting means you will not do any treatment right away, but your doctor will follow your cancer closely and treat any symptoms that you have if it begins to spread. Although it would be too late to be cured, you would be comfortable and free of pain. There are no side effects to watchful waiting, but five percent (5 out of 100) of the people who choose this treatment will develop symptoms and die from their cancer within five years. On the other hand, the surgery would cure your cancer permanently. Following surgery you will feel more tired than usual and will experience stomach upset occasionally for the three months following your surgery. However, surgery has a ten percent (10 out of 100) risk of death during the surgery.

Imagine that both of these treatments are completely covered by your health insurance. Which would you choose?

I would not take the surgery and accept the 5% chance of dying from this cancer.

I would take the surgery and accept the 10% chance of dying from the surgery.

How do your answers compare?

In the real world, cancer patients sometimes choose treatments that may have devastating side effects over less invasive, yet equally or more effective, approaches. One explanation for this is that people may feel a strong need to "get the cancer out" of their bodies. Surgical removal of all potentially cancerous tissues may satisfy this desire so thoroughly that people end up ignoring important statistical information about adverse outcomes.

Making a choice not in their best interest

CBDSM investigators Angela Fagerlin, Brian Zikmund-Fisher, and Peter Ubel hypothesized that people perceive cancer diagnoses as a call to action, and more specifically, a call to get rid of the cancer through surgery, regardless of what statistical information might say to the contrary. Consequently, they predicted that when presented with hypothetical cancer diagnoses, many people would say they would pursue surgery even if such an action would decrease their chance of survival.

To explore the relative frequency of people's willingness to choose surgery when it wasn't in their best interest, the investigators designed a cancer scenario similar to the one you read on the previous page. Participants were presented either a surgery or a medication treatment that would either increase or decrease their chance of survival.

The investigators found that participants who were presented with the opportunity to rid themselves of their cancer through surgery were significantly more inclined to take action than those who were presented with the medication treatment. For example, when the treatment reduced their overall chance of survival, 65% chose the surgery, whereas only 38% chose the medication treatment. This suggests that people's treatment decisions may be based not on the effectiveness of the treatments, but rather on their beliefs about how cancer should be treated. Specifically, cancer diagnoses seem to conjure up a strong desire for active treatment. And people seem to have an intuitive belief that action should not just involve treatment, but surgical removal of the cancer.

Why these findings are important

The results of this study may resonate with many clinicians who have encountered cancer patients who seem to desire treatment for treatment's sake, or who have a preference for surgical intervention even before they learn about the pros and cons of their treatment alternatives. This study should serve to remind clinicians that patients' preference for action can be strong enough, at times, to be a bias. At a minimum, it is important for health care professionals to be aware of the potential for such biases, so they can decide whether to accept patients' preferences at face value, or try to convince patients that aggressively treating a tumor may not be in their best interests.

How do your answers compare?

Making a risk estimate can change the feel of the actual risk

CBDSM investigators Angela Fagerlin, Brian Zikmund-Fisher, and Peter Ubel designed a study to test whether people react differently to risk information after they have been asked to estimate the risks. In this study, half the sample first estimated the average woman's risk of breast cancer (just as you did previously), while the other half made no such estimate. All subjects were then shown the actual risk information and indicated how the risk made them feel and gave their impression of the size of the risk. The graph below shows what they found:

As shown in the graph above, subjects who first made an estimated risk reported significantly more relief than those in the no estimate group. In contrast, subjects in the no estimate group showed significantly greater anxiety. Also, women in the estimate group tended to view the risk as low, whereas those in the no estimate group tended to view the risk as high.

So what's responsible for these findings? On average, those in the estimate group guessed that 46% of women will develop breast cancer at some point in their lives, which is a fairly large overestimate of the actual risk. It appears, then, that this overestimate makes the 13% figure feel relatively low, leading to a sense of relief when subjects find the risk isn't as bad as they had previously thought.

Why this finding is important

Clinical practice implications - The current research suggests that clinicians need to be very deliberate but very cautious in how they communicate risk information to their patients. These results argue that a physician should consider whether a person is likely to over-estimate their risk and whether they have an unreasonably high fear of cancer before having them make a risk estimation. For the average patient who would overestimate their risk, making a risk estimation may be harmful, leading them to be too relieved by the actual risk figure to take appropriate actions. On the other hand, if a patient has an unreasonably high fear of cancer, having them make such an estimate may actually be instrumental in decreasing their anxiety. Physicians may want to subtly inquire whether their patient is worried about her cancer risk or if she has any family history of cancer to address the latter type of patient.

Research implications - Many studies in cancer risk communication literature have asked participants at baseline about their perceived risk of developing specific cancers. Researchers then implement an intervention to "correct" baseline risk estimates. The current results suggest that measuring risk perceptions pre-intervention will influence people's subsequent reactions, making it difficult to discern whether it was the intervention that changed their attitudes or the pre-intervention risk estimate. Researchers testing out such interventions need to proceed with caution, and may need to add research arms of people who do not receive such pre-tests.

For certain diseases, receiving treatment can disrupt daily life considerably. How would this disruption affect your happiness?

Think about your average mood during a typical week. How would you rate your average mood?

very pleasant

slightly pleasant

neutral

slightly unpleasant

very unpleasant

Now imagine you have end-stage renal disease, a condition in which your kidneys fail to perform their normal function of cleaning and filtering the blood. Treatment consists of a procedure called hemodialysis, in which your blood is filtered through a machine. You require treatment three times per week for about three hours each time. Discomfort is minor, and you can read, write, talk, eat, sleep, or watch TV during the treatment. Your lifestyle includes most normal activities, including work, exercise, and leisure; however, you feel fatigued if you miss treatment for several days. Also, you must follow a strict diet that involves reducing salt intake, consuming relatively little meat, and drinking only small amounts of fluids. Imagine, you have been on hemodialysis for a year.

Now imagine your average mood during a typical week if you had end-stage renal disease as described above. If you had end-stage renal disease, how do you think you would rate your average mood?

very pleasant

slightly pleasant

neutral

slightly unpleasant

very unpleasant

How do your answers compare?

The discrepancy between Patients and Non-patients

Past research has shown that there are serious health conditions that do not seem to be as badly experienced by the people living with them as healthy people would expect. Although the existence of this discrepancy is well established at this point, its cause is not. One possibility is that patients are exaggerating their well-being. They may be focusing on periods of positive mood even though they actually experience lengthy periods of negative mood. On the other hand, patients might be as happy as they report and healthy people might very much be overestimating the negative impact of the illness. A related explanation comes from evidence that healthy people tend to underestimate their own past moods, recalling negative times more readily than positive times. This would then make them more likely to also understate the well-being of other people as well, and this could contribute to the discrepancy.

Which explanation is correct?

Jason Riis, a researcher at the University of Michigan, teamed up with investigators from CBDSM and the University of Pennsylvania to conduct a study with the goal of finding out which of the above explanations is accountable for the discrepancy. To accomplish this, they set out to measure mood in two ways. One way is to ask individuals to estimate their average mood. The other way is to measure mood on a momentary basis, asking individuals at frequent intervals to indicate their mood at the moment, and then taking the average of these responses. This latter way of assessing mood is less influenced by biased recall than just asking subjects to estimate overall mood.

The investigators recruited 49 end-stage renal patients receiving hemodialysis treatment three times per week and 49 healthy controls who were matched to the patients on age, race, sex, and education. Subjects were first given an entry interview during which they estimated their average mood. They were then asked to carry around Palm Pilots for a week that beeped at random intervals, prompting them to indicate their mood at the moment. After carrying the Palm Pilots around for a week, subjects completed an exit interview that asked them to recall their average mood in the last week and to again estimate their average mood in general. Healthy subjects also estimated what they thought their average mood would be if they were a hemodialysis patient.

The investigators found that patients' average momentary moods were no lower than their estimated average mood, thus finding no evidence that patients exaggerate their mood. In fact, they failed to find any evidence that patients experience lower moods than healthy controls. In appears, then, that hemodialysis patients do largely adapt to their condition. On the other hand, healthy controls did rate that their average mood would be lower if they were homodialysis patients. Thus, the previously observed tendency of healthy people to underestimate the reported quality of life of people with various health conditions does seem to be due, in large part, to their misperception of the extent to which people can adapt to such conditions. In this study, healthy people also underestimated their own average mood. This could also account for some of the discrepancy, but the effect was not very large.

Why this is important

Ignorance of adaptation can have negative consequences for decision making. It can cause individuals to opt for unnecessarily risky surgeries and policymakers to invest in programs that have a minimal impact on people's well-being. This is not to say that research and treatment of kidney disease should not continue to be priorities, but in making difficult policy decisions, consideration of the moods experienced by patients may influence priorities between serious conditions such as, for example, paraplegia and depression. The results of this study suggest that policy makers should proceed with caition because healthy people's apparent exaggeration of the influence of illness on mood can lead to incorrect perceptions of how illness will influence quality of life.

A longer life may result from the amount of social support present in your life, but is the longevity due to giving or receiving that support?

Imagine that in your busy schedule each week, you typically at least have Wednesday and Saturday nights free as time to spend however you want. Recently, however, one of your close friends had her car break down and now she is wondering whether you would be willing to drive her to and from a yoga class on Wednesday nights for the next three weeks while the car is in the shop. She told you that the class is only about a 15 minute drive each way. She said that you shouldn't feel pressured, and she just thought she'd ask if you had the time to help her out.

Would you be willing to drive your friend to and from her yoga class for the next three weeks?

Yes, I'd take the time to help her out.

No, I'd keep my Wednesday nights free.

Do you think that helping out others could at all affect your health?

Yes

No

Giving vs. receiving: effects on mortality

A research team of investigators at the U of M Institute for Social Research teamed up with CBDSM investigator, Dylan Smith, to conduct a study investigating whether giving or receiving help affects longevity. The researchers noted that receiving social support is likely to be correlated with other aspects of close relationships, including the extent to which individuals give to one another. Based on this, they hypothesized that some of the benefits of social contact, sometimes attributed to receiving support from others, may instead be due to the act of giving support to others.

Using a sample of 423 married couples from the Detroit area, the investigators conducted face-to-face interviews over an 11-month period. The interviews assessed the amount of instrumental support respondents had given to and received from neighbors, friends, and relatives, as well as the amount of emotional support they had given to and received from their spouse. Instrumental support included things like helping with transportation, errands, and child care, whereas emotional support involved having open discussions with a spouse and feeling emotionally supported. Mortality was monitored over a 5-year period by checking daily obituaries and monthly death record tapes provided by the State of Michigan. To control for the possibility that any beneficial effects of giving support are due to a type of mental or physical robustness that underlies both giving and mortality risk, the investigators also measured a variety of demographic, health, and individual difference variables, including social contact and dependence on the spouse.

The investigators found that those who reported giving support to others had a reduced risk of mortality. This was true for both instrumental supoprt given to neighbors, friends, and relatives, and for emotional support given to a spouse. They also found that the relationship between receiving social support and mortality depended on other factors. Specifically, receiving emotional support appeared to reduce the risk of mortality when dependence on spouse, but not giving emotional support, was controlled. Receiving instrumental support from others actually increased the risk of mortality when giving support, but not dependence on spouse, was controlled.

What can we make of these findings?

It appears from these results that the benefits of social contact are mostly associated with giving rather than receiving. Measures that assess receiving alone may be imprecise, producing different results as a function of dependence and giving support.

Given the correlational nature of this study, it is not possible to determine conclusively that giving support accounts for the social benefit traditionally associated with receiving support. Nevertheless, the results of the present study should be considered a strong argument for the inclusion of measures of giving support in future studies of social support, and perhaps more importantly, researchers should be cautious of assuming that the benefits of social contact reside in receiving support.

It's true that when helping others out, you might have to give up some of your own time, but based on the above findings, it looks like in the long run you may end up ultimately gaining more time.